When working with large sets of data, such as a database of documents or web pages on the Internet, the volume of available data can make it difficult to find information of relevance. Various methods of searching are used in an attempt to find relevant information in such stores of information. Some of the best known systems are Internet search engines, such as Yahoo (trademark) and Google (trademark) which allow users to perform keyword-based searches. These searches typically involve matching keywords entered by the user with keywords in an index of web pages.
However, existing Internet search methods often produce results that are not particularly useful. The search may return many results, but only a few or none may be relevant to the user's query. On the other hand, the search may return only a small number of results, none of which are precisely what the user is seeking while having failed to return potentially relevant results.
One reason for some difficulties encountered in performing such searches is the ambiguity of words used in natural language. Specifically, difficulties are often encountered because one word can have several meanings. This difficulty has been addressed in the past by using a technique called word sense disambiguation, which involves changing words into word senses having specific semantic meanings. For example, the word “bank” could have the sense of “financial institution” attached to it, or another definition.
U.S. Pat. No. 6,453,315 teaches meaning based information organization and retrieval. This patent teaches creating a semantic space by a lexicon of concepts and relations between concepts. Queries are mapped to meaning differentiators which represent the location of the query and the semantic space. Searching is accomplished by determining a semantic difference between differentiators to determine closeness and meaning. This system relies upon the user to refine the search based on the meanings determined by the system or alternatively to navigate through nodes found in the search results.
As known in the art, the evaluation of the efficiency of information retrieval is quantified by “precision” and “recall”. Precision is quantified by dividing the number of correct results found in a search by the total number of results. Recall is quantified by dividing the number of correct results found in a search by the total number of possible correct results. Perfect (i.e. 100%) recall may be obtained simply by returning all possible results, except of course, this will give very poor precision. Most existing systems strive to balance the criteria of precision and recall. Increasing recall, for example by providing more possible results by use of synonyms, can consequentially reduce precision. On the other hand, increasing precision by narrowing the search results, for example by selecting results that match the exact sequence of words in a query, can reduce recall.
There is a need for a query processing system and method which addresses deficiencies in the prior art.